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  Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)

Pehl, M., Schreyer, F., & Luderer, G. (2024). Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0). Geoscientific Model Development, 17(5), 2015-2038. doi:10.5194/gmd-17-2015-2024.

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資料種別: 学術論文

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gmd-17-2015-2024.pdf (出版社版), 5MB
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gmd-17-2015-2024.pdf
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公開
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application/pdf / [MD5]
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関連URL

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説明:
The mrremind packages contains data preprocessing for the REMIND model.
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Software

作成者

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 作成者:
Pehl, Michaja1, 著者              
Schreyer, Felix1, 著者              
Luderer, Gunnar1, 著者              
所属:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

内容説明

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 要旨: This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).

資料詳細

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言語: eng - 英語
 日付: 2023-07-142024-01-122024-03-072024-03-07
 出版の状態: Finally published
 ページ: 24
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.5194/gmd-17-2015-2024
MDB-ID: No MDB - stored outside PIK (see DOI)
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Working Group: Energy Systems
Model / method: REMIND
Regional keyword: Global
Research topic keyword: Energy
Research topic keyword: Decarbonization
Research topic keyword: Economics
OATYPE: Gold Open Access
 学位: -

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Project information

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Project name : ARIADNE
Grant ID : 03SFK5A
Funding program : -
Funding organization : Bundesministerium für Bildung und Forschung (BMBF)
Project name : ECEMF
Grant ID : 101022622
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

出版物 1

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出版物名: Geoscientific Model Development
種別: 学術雑誌, SCI, Scopus, p3, oa
 著者・編者:
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出版社, 出版地: -
ページ: - 巻号: 17 (5) 通巻号: - 開始・終了ページ: 2015 - 2038 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals185
Publisher: Copernicus